Biomimetics is an emerging field of science that adapts the working principles from nature to fine-tune the engineering design aspects to mimic biological structure and functions. The application mainly focuses on the development of medical implants for hard and soft tissue replacements. Additive manufacturing or 3D printing is an established processing norm with a superior resolution and control over process parameters than conventional methods and has allowed the incessant amalgamation of biomimetics into material manufacturing, thereby improving the adaptation of biomaterials and implants into the human body. The conventional manufacturing practices had design restrictions that prevented mimicking the natural architecture of human tissues into material manufacturing. However, with additive manufacturing, the material construction happens layer-by-layer over multiple axes simultaneously, thus enabling finer control over material placement, thereby overcoming the design challenge that prevented developing complex human architectures. This review substantiates the dexterity of additive manufacturing in utilizing biomimetics to 3D print ceramic, polymer, and metal implants with excellent resemblance to natural tissue. It also cites some clinical references of experimental and commercial approaches employing biomimetic 3D printing of implants.

In this study, fused filament fabrication (FFF) printing parameters were optimized to improve the surface quality and reduce the printing time of Acrylonitrile Butadiene Styrene (ABS) polymer using the Analysis of Variance (ANOVA), it is a statistical analysis tool. A multi-objective optimization technique was employed to predict the optimum process parameter values using particle swarm optimization (PSO) and response surface methodology (RSM) techniques. Printing time and surface roughness were analyzed as a function of layer thickness, printing speed and nozzle temperature. A central composite design was preferred by employing the RSM method, and experiments were carried out as per the design of experiments (DoE). To understand the relationship between the identified input parameters and the output responses, several mathematical models were developed. After validating the accuracy of the developed regression model, these models were then coupled with PSO and RSM to predict the optimum parameter values. Moreover, the weighted aggregated sum product assessment (WASPAS) ranking method was employed to compare the RSM and PSO to identify the best optimization technique. WASPAS ranking method shows PSO has finer optimal values [printing speed of 125.6 mm/sec, nozzle temperature of 221 °C and layer thickness of 0.29 mm] than the RSM method. The optimum values were compared with the experimental results. Predicted parameter values through the PSO method showed high surface quality for the type of the surfaces, i.e., the surface roughness value of flat upper and down surfaces is approximately 3.92 µm, and this value for the other surfaces is lower, which is approximately 1.78 µm, at a minimum printing time of 24 min.